结合共现图的动态规划越南语文本重音恢复新方法

Ho Trong Nghia, Do Phuc
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引用次数: 4

摘要

本文提出了一种新的越南语文本重音恢复方法。给定一个丢失了重音的越南语文本,我们的目标是寻找一个产生最佳词法概率的恢复文本。本文首先采用动态规划的方法,建立了越南语的语言模型作为词汇数据库,给出了越南语句子的词汇概率。其次,我们构建了一个越南语单词直译的地图来限制我们的搜索空间。最后,我们将动态规划作为搜索引擎来寻找最可能的句子。我们还利用共现图来提高选择的准确率,实验结果表明,我们的方法的平均准确率约为93%-94%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Approach To Accent Restoration Of Vietnamese Texts Using Dynamic Programming Combined With Co-Occurrence Graph
In this paper, we would like to introduce a new approach to recover Vietnamese text's accents. Given a Vietnamese text in which accents are lost, our goal is to seek for a recovered text that yields a best lexical probability. Using a dynamic programming approach, we first build a model of language for Vietnamese as a lexical database which gives lexical probabilities to Vietnamese sentences. Second, we construct a map of literal translations of Vietnamese words to restrict our searching space. Finally, we apply dynamic programming as a searching engine to seek out the most probable sentence. We also use the co-occurrence graph to increase the accuracy of selection, the experimental results show that the average accuracy of our approach is about 93%-94%.
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